For word-level NMT models, the translation of out-of-vocabulary words has been addressed through a back-off to a dictionary look-up [1]}, [2]}.
We note that such techniques make assumptions that often do not hold true in practice.
For instance, there is not always a 1-to-1 correspondence between source and target words because of variance in the degree of morphological synthesis between languages, like in our introductory compounding example.
Also, word-level models are unable to translate or generate unseen words.
Copying unknown words into the target text, as done by [1]}, [2]}, is a reasonable strategy for names, but morphological changes and transliteration is often required, especially if alphabets differ.
